Dynamically-generated files for visualization sharing

ABSTRACT

Systems and methods are disclosed for generating one or more files to visualize query results. The systems and methods can include parsing one or more files that include one or more queries and computer-executable instructions for displaying results of the one or more queries. The one or more queries can identify a set of data to be processed and a manner of processing the set of data. The systems and methods can further include generating one or more files that include the results of the queries and computer-executable instructions for displaying one or more visualizations of the results.

FIELD

At least one embodiment of the present disclosure pertains to one ormore tools for facilitating searching and analyzing large sets of datato locate data of interest.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include performance data,diagnostic data, or any other data that can be analyzed to diagnoseequipment performance problems, monitor user interactions, and to deriveother insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding and effectively utilizing the data. To reducethe potentially vast amount of data that may be generated, some datasystems pre-process data based on anticipated data analysis needs. Inparticular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it.

Although the availability of vastly greater amounts of diverse data ondiverse data systems provides opportunities to derive new insights, italso gives rise to technical challenges to search and analyze the data.Tools exist that allow an analyst to search data systems separately andcollect results over a network for the analyst to derive insights in apiecemeal manner. However, UI tools that allow analysts to quicklysearch and analyze large set of raw machine data to visually identifydata subsets of interest, particularly via straightforward andeasy-to-understand sets of tools and search functionality do not exist.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12A and 12B illustrate example user interfaces depictingvisualizations of results of one or more queries;

FIG. 13A is a diagram illustrative of an embodiment of at least aportion of a dashboard file;

FIG. 13B is a diagram illustrative of an embodiment of at least aportion of a dynamically-generated dashboard file;

FIG. 14 is a flow diagram illustrative of an embodiment of a routineexecuted by one or more computing devices for generating a set of one ormore files; and

FIG. 15 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which one or moreimplementations may be embodied.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

2.1. Host Devices

2.2. Client Devices

2.3. Client Device Applications

2.4. Data Server System

2.5. Data Ingestion

2.5.1. Input

2.5.2. Parsing

2.5.3. Indexing

2.6. Query Processing

2.7. Field Extraction

2.8. Example Search Screen

2.9. Data Modelling

2.10. Acceleration Techniques

2.10.1. Aggregation Technique

2.10.2. Keyword Index

2.10.3. High Performance Analytics Store

2.10.4. Accelerating Report Generation

2.11. Security Features

2.12. Data Center Monitoring

2.13. Cloud-Based System Overview

2.14. Searching Externally Archived Data

2.14.1. ERP Process Features

3.0 Dynamically-generated Files for Sharing

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate an invertedindex or keyword index to facilitate fast keyword searching for eventdata. To build a keyword index, at block 314, the indexer identifies aset of keywords in each event. At block 316, the indexer includes theidentified keywords in an index, which associates each stored keywordwith reference pointers to events containing that keyword (or tolocations within events where that keyword is located, other locationidentifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”. Further the keyword index or an inverted index can includefield-value pairs, related to one or more fields associated with theevents, such as host, source, and source type of the event, etc.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “Site-Based Search Affinity”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “Multi-SiteClustering”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. Once a data model object is selected by the user, a userinterface screen 700 shown in FIG. 7A may display an interactive listingof automatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “Distributed HighPerformance Analytics Store”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “Compressed Journaling InEvent Tracking Files For Metadata Recovery And Replication”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “Real Time Searching AndReporting”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “Key Indicators View”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CorrelationFor User-Selected Time Ranges Of Values For Performance Metrics OfComponents In An Information-Technology Environment With Log Data FromThat Information-Technology Environment”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“Correlation For User-Selected Time Ranges Of Values For PerformanceMetrics Of Components In An Information-Technology Environment With LogData From That Information-Technology Environment”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “ExternalResult Provided Process For RetriEving Data Stored Using A DifferentConfiguration Or Protocol”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the ]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0 Dynamically-Generated Files for Sharing

As noted above, the present disclosure enables large data sets to bequeried, and for visualizations or other representations of queryresults to be created that provide graphical indicia of the queryresults to an end user. These visualizations or other representations ofquery results, which may also be referred to as“query result displayobjects,” may include any textual or graphical representation of thequery results. In some cases, a single visualization can be generatedand displayed (such as a chart, graph, or listing of search results) ora collection of visualizations can be displayed in relation to oneanother (such as a “Dashboard”). In some cases, each visualization cancorrespond to a distinct query that identifies a set of data to beprocessed and a manner of processing the set of data. In certainembodiments, multiple visualizations can correspond to the same query orbe generated using results of the same query. Accordingly, a singledashboard can be associated with one or more queries corresponding toone or more of the visualizations included therein.

Visualizations may be helpful to enable an end user to quicklycomprehend important aspects of a data set, without being required tomanually review the entire data set. For example, visualizations mayenable an administrator to quickly identify errors within a computingsystem that would otherwise be “buried” within logs of the system.

In many instances, it is beneficial not only to provide visualizations,but also to enable a user to share the visualizations with others orenable multiple users to access or view the visualizations. For example,it may be beneficial for a team to receive automated updates ofvisualizations relevant to a current project at predetermined intervals.To enable this functionality, one possibility is for multiple users toaccess the visualizations individually. However, each time avisualization is accessed, the data intake and query system can run thequeries corresponding to the visualization, which can further tax thecomputing resources of the data intake and query system. Furthermore,some of the users may not have access to the data intake and querysystem with which to run the queries. Thus, it is beneficial to enable acomputing device to identify visualizations to be made accessible tousers and make the visualizations available to the users withoutincreasing the computational burden on the data intake and query systemor without requiring each user to have access to the data intake andquery system.

As a non-limiting example, a computing device can parse one or moredashboard files used to generate and display one or more visualizationsto a user. The dashboard files can include one or more queriesassociated, as well as computer-executable instructions for generatingand displaying visualizations of the query results and for providinginteractive features for the visualizations. In parsing the files, thesystem can identify queries for execution, identify computer-executableinstructions or references to computer-executable instructions forgenerating the visualizations, and identify interactive features of thevisualizations for retention. Further, in some cases, the system canidentify queries, visualizations, and/or interactive features of thevisualizations to be discarded.

For the queries to be retained, the system can obtain the results of thequeries. For example, the system can initiate the queries using the dataintake and query system and receive the results. For the visualizationsand the interactive features identified for retention, the system canobtain the computer-executable instructions for generating anddisplaying the visualizations and for providing the interactivefeatures. In some cases, the system can access the relevant directoriesand files that store the computer-executable instructions for generatingand displaying the visualizations and providing the interactivefeatures.

Using the query results and the computer-executable instructions forgenerating and displaying the visualizations and providing theinteractive features, the system can generate one or more dashboardfiles. The generated files can include the results of the queries andthe obtained computer-executable instructions for generating anddisplaying the visualizations. In some cases, the files can omit thequeries themselves and instead include the “hard coded” visualizationsof the query results. Furthermore, the files can include thecomputer-executable instructions for providing the interactive featurescorresponding to the visualizations.

The generated dashboard files can be stored in a location accessible tothe users. For example, the generated files can be stored on a localnetwork drive or network accessible server, embedded in an email, etc.By including the results of the queries and the computer-executableinstructions for generating and displaying the visualizations (includinginteractive features) in the generated dashboard files, the system canreduce the number of queries performed and make the visualizationsavailable to users that do not have access to the data intake and querysystem.

Upon accessing the generated dashboard files, a computing device canexecute the computer-executable instructions to generate and display thevisualizations, including the interactive functionality that wasidentified for retention. Accordingly, a user can view and interact withthe visualizations as if she had executed the queries, but withoutaccessing the data intake and query system or executing the queries.

As will be appreciated by one of skill in the art in light of thepresent disclosure, the embodiments disclosed herein improve the abilityof computing systems, such as a SPLUNK® ENTERPRISE system, to achievefunctionality not previously possible on a computing device.Specifically, aspects of the present disclosure enable the automatedgeneration of files that include query results and computer-executableinstructions for displaying visualizations of the query results. Thepresently disclosed embodiments address technical problems inherentwithin computing systems; specifically, the difficulty of reducingcomputational tasks of a data intake and query system, while providingvisualizations that rely on the data intake and query system to usersthat may not have access to the data intake and query system. Thesetechnical problems are addressed by the various technical solutionsdescribed herein, including the identifying queries and obtainingrelevant computer-executable instructions to generate visualizations ofthe results of the queries, and generating one or more files thatinclude the query results and the computer-executable instructions forgenerating the visualizations. Thus, the present disclosure representsan improvement on existing data processing systems and computing systemsin general.

FIGS. 12A and 12B illustrate example user interfaces depictingvisualizations of results of one or more queries. Each of the interfacesof FIGS. 12A and 12B may be generated on a computing device (e.g., aclient device 102) based on execution of an application or othercomputer-executable code, such as the computer-executable code describedin greater detail below with reference to FIGS. 13A and 13B. In oneembodiment, each of the interfaces illustrated in FIGS. 12A and 12B maybe generated by a browser application executing on a computing device,and may represent a rendering of a network data object, such as ahypertext markup language (HTML) file. As will be appreciated by oneskilled in the art, an HTML file or other network data object mayinclude markup language denoting a structure of information to bedisplayed when the file is rendered by a browser application. Thenetwork data object may be made available, for instance, at a uniformresource identifier (URI) of a communications network. In someinstances, an HTML file or other network data object may include localcomputer-executable code, sometimes referred to as “client-sidescripting,” that is executable by a computing device to providefunctionality to the interface. One common example format of suchclient-side scripting is the JAVASCRIPT™ code format. In some instances,a network data object may include or reference additional network dataobjects, such as extensible markup language (XML) files or cascadingstyle sheet (CSS) files, that work in combination to define aninterface. In the illustrative examples of FIGS. 12A and 12B, it will beassumed that the interfaces represent renderings of an HTML fileincluding or referencing a XML content that (at least partly) defines astructure of the interfaces. It will further be assumed that the HTMLfile includes or references client-side scripting executable tointerpret the XML content and generate the interface.

Illustratively, the client-side scripting may read from an XML file anumber of elements to be included in the interface, and populate theelements with relevant data. In some instances, that data may be aresult of a query executed by a data intake and query system on rawmachine data or indications of raw machine data, and thus may beretrieved from a network resource, such as a search head 210 of FIG. 2.As discussed below, the client-side scripting may further be executable(independently or in conjunction with code executing on a remote device)to parse the dashboard files, initiate queries within the dashboardfiles, and generate dashboard files that include the query resultsembedded therein with computer-executable instructions to generate andrender visualizations of the query results.

With reference to FIG. 12A, an illustrative “dashboard” view 1200 userinterface is shown. Generally described, a dashboard may refer to aninterface that depicts a collection of one or more visualizations of oneor more underlying data sets (such as raw machine data). In oneembodiment, the visualizations are generated by application of alate-binding schema to the data set, in accordance with embodimentsdescribed above. In another embodiment, the visualizations are generatedbased on executing a query on structured or pre-processed data, such asby parsing, searching, processing, or filtering information within arelational database, keyword index, and/or inverted index.

For the purposes of illustration, the dashboard view 1200 is depicted asa visualization of information regarding a network resource site,denoted in FIG. 12A as “My Site” 1216. The site may, for example,correspond to a web site available on a global area network (GAN), suchas the Internet. In this regard, the dashboard view 1200 may providevisualizations of server logs or access logs corresponding to the site.In FIG. 12A, three visualizations are included within the dashboard view1200: an indicators visualization 1202, an access overview visualization1204, and a browser type visualization 1206, each discussed in moredetail below.

The indicators visualization 1202 of FIG. 12A provides a number of“indicators” regarding underlying data, such as a number of total viewsof a site, a number of “views today” of the site, number of “errorstoday,” and a most common error. In the illustration of FIG. 12A, thetotal views may indicate a total number of accesses of the site (e.g.,of a network data object corresponding to the site) by client computingdevices, as indicated in underlying data. The “views today” may indicateaccesses to the site in a 24 hour period, as indicated in the underlyingdata. The “errors today” may indicate errors in accessing the site, asindicated in the underlying data. The “most common error” may reflect anerror type identifier most common among the errors in a given 24 hourperiod, as indicated in the underlying data. In FIG. 12A, the “mostcommon error” type is shown as error type 503, an error defined by thehypertext transport protocol (HTTP) that indicates “serviceunavailable.” Each indicator may be generated, for example, by executinga query on raw machine data in accordance with embodiments describedabove.

The access overview visualization 1204 of FIG. 12A represents variousaccesses to the site by different users (e.g., each representing a setof access credentials to the site, a specific client computing device,etc.). Specifically, each row of the table shown in the overviewvisualization 1204 may include a username (denoted as “user.name,” where“user” can represent an object in accordance with an object-orientedprogramming model and name can represent an attribute of the object), anerror count (denoted as “errorCount”) indicating errors returned to adevice of the user, and a number of views (denoted as “numberOfViews”)indicating access of the site by a device of the user. The accessoverview visualization 1204 further includes a control element 1210selectable to view different rows of the table shown in thatvisualization. For example, a user could select “2” of the element 1210to view additional rows, each corresponding to a username, error count,and number of views. Thus, the access overview visualization 1204provides an intuitive review of logs of the site grouped according touser name.

The browser type visualization 1206 depicts another visualization ofunderlying data regarding operation of the site. Specifically, thebrowser type visualization 1206 depicts a pie chart of various “browsertypes” accessing the site. Illustratively, each slice of the pie chartcan represent a different browser application used to access the site(or, potentially, a different “user-agent” string included in requeststo access the site), and the size of the slice can represent an overallproportion of total accesses of the site represented by each browserapplication. Thus, the browser type visualization 1206 provides anintuitive review of the types of browser applications accessing thesite.

To enable further review of information regarding the site, a user mayinteract with one or more of the data elements or visualizations shownin FIG. 12A to view additional information regarding that data element.The interactivity associated with the data elements can also be referredto as interactive functionality. For example, each of the numbers withinthe indicators visualization 1202 may be selectable (e.g., hovered over,clicked on, etc.) to view additional information regarding thatindicator. Each of the entries in the table of the access overviewvisualization 1204 (e.g., each username, error count and number ofviews) may be selectable to view further information regarding thatentry. Each of the slices of the pie chart of the browser typevisualization 1206 may be selectable to view additional informationregarding a browser application type represented by the slices. In somecases, selection of the data element within the dashboard view 1200 maycause display of additional information within the dashboard view 1220,such as a tooltip, etc. In certain embodiments, selection of a dataelement within the dashboard view 1220 may cause display of a second setof visualizations that use, as an input, data associated with theselected data element.

One example of such a second set of visualizations is shown in FIG. 12B,as the “user view” dashboard 1220. Specifically, the user view dashboard1220 may provide a visualization of at least a portion of the data set(e.g., access logs to the site) with respect to an individual user ofthe site, such as an entity defined by a set of access credentials or anindividual client computing device. In the example of FIG. 12B, the userview dashboard 1220 may be displayed based on a user selection of a dataelement within the access overview visualization 1204 of FIG. 12A thatrepresents an individual user. For example, the user view dashboard 1220may be displayed based on a user selection of data element 1208 of FIG.12A, representing username “userF.” The selected data element may thenbe used to customize the information represented within the user viewdashboard 1220.

In the example of FIG. 12A, a user identifier (denoted in FIG. 12B as“userID84”) that corresponds to “userF” has been retrieved, and used asan input within the input field 1222. Thus, the data shown in thedashboard 1220 reflects portions of underlying data associated with theuser identifier. Specifically, the dashboard 1220 includes a set ofindicators 1224 associated with the user identifier (including totalviews, views today, errors, and a most common error). Illustratively,data within the input field 1222 may be altered to view indicatorscorresponding to another user. While the indicators 1224 are depicted assimilar to indicators 1202 of FIG. 12A (albeit reflecting a single userrather than all accesses to a site), different dashboards may in otherembodiments include different indicators regarding underlying data.

The user view dashboard 1220 further includes a “view details”visualization 1228, providing information regarding accesses to a siteby the user identified in the input field 1222. Each row of the viewdetails visualization 1228 can reflect a single transaction between aclient device associated with the user identifier and a serverassociated with the site. The columns within the view detailsvisualization 1228 illustrated reflect a time of that transaction, anetwork data object requested by the client device, an error detected inthe transaction (if any), and a time (in milliseconds) needed by theserver to respond to the request. Like the data elements of FIG. 12A,the data elements of FIG. 12B (e.g., the indicators 1224 and theindividual entries of the view details visualization 1228) may beselectable to view additional information within a data set pertainingto a selected data element. Additional details regarding the user viewdashboard 1220 are described in U.S. App. No. TBD, filed concurrentlyherewith, entitled LINKING DATA SET SUMMARIZATIONS USING AFFINITIES(Attorney Docket No. SPLK.018A1), which is incorporated by referenceherein in its entirety.

FIG. 13A is a diagram illustrative of an embodiment of at least aportion of a dashboard file 1300 that generally corresponds to dashboardview 1200. The dashboard file 1300 includes various queries 1302, 1304that are associated with the visualizations 1202, 1204, respectively,shown in the dashboard view 1200, computer-executable code 1306, 1308 toinitiate the various queries (also referred to as query initiationcode), and computer-executable code, such as code 1310A, 1310B, togenerate and display the visualizations based on the results of thequeries (also referred to as visualization code).

Upon accessing the dashboard file 1300, a computing device can beginexecuting the code included therein. For example, the computing devicecan execute the query initiation code 1306, 1308, which will cause thecomputing device to initiate (or request the initiation of) the queries1302, 1304 by the data intake and query system. As described in greaterdetail above, the queries 1302, 1304 can identify a set of data forprocessing and a manner of processing the set of data. Further, thecomputing device can use the visualization code, such as code 1310A,1310B, to display, or to access additional code to display, the resultsof the queries 1302, 1304.

In some cases, the code in the dashboard file 1300 can includereferences, links, or calls to additional files or computer codelibraries to complete a particular function. For example, thevisualization code 1310A, 1310B can refer to a different file or librarythat is used to display a particular portion of a visualization or thevisualization as a whole. Upon encountering such a link, reference, orcode, the computing device can access the identified file or library tocomplete the corresponding function.

In some cases, the files or libraries can include computer-executablecode in a different format, such as a different computer language andcan include more detailed computer-executable instructions forperforming the particular function (non-limiting example: generating anddisplaying a visualization). For example, in some cases, the dashboardfile 1300 can be implemented using HTML and/or XML, and can includelinks, references, or calls to JavaScript or CSS libraries or files togenerate and display the visualizations. In this way the dashboard file1300 can be a relatively small or short file compared to a file thatincludes all of the computer-executable instructions for initiating thequeries and generating and displaying the visualizations.

Upon accessing the dashboard file 1300 the query initiation code 1306,1308 can be executed and the queries 1302, 1304 initiated. Further, insome embodiments, each time the dashboard file 1300 is accessed, thequeries 1302, 1304 are initiated and the visualizations 1202-1206 aregenerated and displayed. As mentioned above, the queries can increasethe computational demands and processing of the data intake and querysystem, and in some embodiments, a user may not have the credentials toaccess the data intake and query system. In certain cases, a user thataccesses the dashboard file 1300 may be required to enter credentials toinitiate the queries 1302, 1304 on the data intake and query system orreceive an error because the data intake and query system could not beaccessed.

Accordingly, in some embodiments, one or more computing devices of thedata intake and query system, such as one or more indexers or searchheads, or one or more computing devices in communication with the dataintake and query system can generate one or more dashboard files that donot require access to the data intake and query system to display thevisualizations 1202-1206. In certain embodiments, such dashboard filesmay also referred to herein as dynamically-generated dashboard files,derivative dashboard files, or self-contained dashboard files.

In some cases, to generate the derivative dashboard file, one or morecomputing devices can parse one or more source dashboard file(s),initiate the queries referenced therein, and generate derivativedashboard file(s) based on the results of the queries and thevisualizations referenced by the source dashboard file(s).

As part of parsing the source dashboard file, the computing devices canidentify the various commands and references found in the sourcedashboard file and perform different operations accordingly. Forexample, the computing devices can identify queries, query initiationcode, and visualization code. To identify the different portions of thedashboard file, the computing devices can reference a library orconfiguration file that describes the different types of code orcommands. In some embodiments, the configuration file can include thedifferent commands that can be found in a dashboard file, and computerinstructions related to how to identify the different commands, how toparse the different commands, how to determine when one command ends andanother begins, and what to do with the different commands (access adifferent library of code to effectuate the action referenced by thecode, access another dashboard file, discard the command, etc.).

With reference to the illustrated example of FIG. 13A, a configurationfile can identify the command <query> as query initiation code,“index=_internal|head 1000 . . . ” as a query, <option name=“colorBy . .. ”> as visualization code, <drilldown> as interactivity code that mayor may not require access to another dashboard file and/or the dataintake and query system. The configuration file can further includeinstructions regarding how to parse the different code or queries, andwhat is to be done with the different code. For example, theconfiguration file can include location information of where relevantcode can be obtained, such as a particular directory or file, in orderto effectuate the actions referenced by the code. As yet anotherexample, the configuration file can include instructions regarding whichcode includes actions that are to be discarded and which code includesactions that are to be included in a derivative dashboard file.

In some embodiments, the configuration file can include definitions ofdifferent elements found within the source dashboard file. For example,the configuration file can include the definitions and different partsof <single>, <chart>, <table>, <viz>, “charting.chart,” or otherstructures. The definitions can include what elements are expected fromthe different structures and what code can be used to implement thestructures. In some embodiments, as the computing devices parse thesource dashboard file, it tracks the different structures containedtherein in order to access the relevant code to implement thestructures.

In some embodiments, with respect to queries and query initiation code,the computing devices can initiate the queries and obtain the results.For example, the computing devices can request the data intake and querysystem to execute the queries and receive the results of the queriesfrom one or more indexers or search heads. With respect to thevisualization code, the computing devices can identify any libraries orfiles referenced by the visualization code and access the identifiedfiles or libraries to obtain the code to generate and display thedifferent visualizations.

In some cases, the visualization code can also include or referencecomputer-executable instructions to provide interactive functionalitywith respect to the visualizations (also referred to as interactivitycode), such as, but not limited to drilldown functionality, tooltipfunctionality, zoom-in functionality, etc. The computing devices candetermine that some interactive functionality is to be included in thedynamically-generated dashboard file and some is not.

In some embodiments, the computing devices can determine thatinteractivity code that requires access to the data intake and querysystem is not to be included and interactivity code that does notrequire access to the data intake and query system is to be included.For example, in some embodiments, the <drilldown> command 1312 mayrequire access to the data intake and query system and/or to anotherdashboard file in order to generate the dashboard illustrated in FIG.12B. Accordingly, in some embodiments, the computing devices candetermine that the functionality or result of the <drilldown> command1312 is not to be included in the derivative dashboard file.

In contrast, the computing devices can determine that some of thevisualization code references interactive functionality, such as zoom-inor tooltips functionality, that does not require access to the dataintake and query system, and can include the code to provide theinteractive functionality in the derivative dashboard file.

In certain embodiments, the computing devices can determine that thefunctionality of the <drilldown> command 1312, or other interactivefunctionality that requires access to the data intake and query system,is to be included. In such embodiments, the computing devices can accessthe data intake and query system and include any query results in thederivative dashboard file. Further, to the extent the interactivefunctionality references another dashboard file (as shown in <drilldown>command 1312), the computing devices can obtain the relevant dashboardfile, parse it, and generate a second derivative dashboard file based onthe dashboard file referenced in the first dashboard file and/or includethe code for the visualizations of the referenced dashboard in the firstderivative dashboard file. With reference to FIG. 13A, in some cases,the computing devices can access the dashboard file referenced by the<drilldown> command 1312. This referenced dashboard file can refer tothe dashboard file used to generate the dashboard shown in FIG. 12B. Thecomputing devices can parse the dashboard file corresponding to thedashboard shown in FIG. 12B and either generate a second derivativedashboard file that when accessed can display a dashboard that lookssimilar to the dashboard shown in FIG. 12B and includes relevant queryresults inline, or include the relevant code to generate the dashboardshown in FIG. 12B into the first derivative dashboard file, such as thederivative dashboard file shown in FIG. 13B.

For the interactive functionality that is to be included in thedynamically-generated dashboard, the computing devices can identify anyfiles or libraries referenced by the interactivity code and access theidentified files or libraries to obtain the code to implement theinteractivity functionality. By including interactivity code in thedynamically-generated dashboard file, the corresponding interactivefunctionality can be provided to a user that accesses thedynamically-generated dashboard file. In this way, thedynamically-generated dashboard can provide additional functionality toa user beyond what a snapshot of the visualizations, such as a PNG orPDF of the visualizations, could provide. Accordingly, despite the queryresults in the dynamically-generated dashboard file being static (e.g.,not dynamically generated or obtained from the data intake and querysystem at run-time), the dashboard can provide the user withfunctionality that mimics the functionality of the source dashboard filethat obtains the query results from the data intake and query system atrun-time.

For the interactive functionality that is not to be included in thedynamically-generated dashboard, the computing devices can pass over thecorresponding interactivity code without identifying code to be includedin the dynamically-generated dashboard file. Similarly, the computingdevices can pass over other code in the dashboard file that implementsother functionality, such as queries or other visualizations, that isnot to be included in the dynamically-generated dashboard.

In some embodiments, the interactivity functionality can includeaccessing an additional dashboard, such as accessing dashboard 1220 fromdashboard 1200. In certain embodiments, such as where the drilldownfunctionality is to be included in the dynamically-generated dashboardfile, the computing devices can obtain the dashboard file thatcorresponds to the additional dashboard, parse it similar to the mannerin which the first dashboard file is parsed, and generate a separatedynamically-generated dashboard file corresponding to the additionaldashboard. In some cases, the computing devices can include the codegenerated from the additional dynamically-generated dashboard file inthe first dynamically-generated dashboard file.

By parsing additional source dashboard files referenced by the originalsource dashboard file, the functionality of the dynamically-generateddashboard can be further increased. For example, when a user selects thedrilldown feature of the dynamically-generated dashboard 1200 thatreferences the dynamically-generated dashboard 1220, the computingdevice can execute the dynamically-generated dashboard filecorresponding to the dynamically-generated dashboard 1220. As such, thefunctionality of the source dashboard can be retained even if the dataintake and query system is not made available and/or the query resultsused for the dynamically generated dashboard files are static.

The computing devices can parse as many dashboard files that arereferenced or accessed by the different dashboard files. For example,the computing devices can follow each reference to a dashboard file inanother dashboard file until all dashboard files referenced by any otherdashboard file are parsed. Similarly, in certain embodiments, thecomputing devices can parse up to a predetermined number of dashboardfiles or up to a predetermined level of dashboard files. For example,the computing devices can identify three levels of dashboard files toparse, meaning that the computing devices will parse a first dashboardfile (first level) any dashboard files referenced by the first dashboardfile (second level), and any dashboard files referenced by the secondlevel of dashboard files (third level). It will be understood that thepredetermined number of dashboard files or predetermined levels can beadjusted as desired.

Having parsed the source dashboard file(s), the computing devices cangenerate the dynamically-generated dashboard file(s). FIG. 13B is adiagram illustrative of an embodiment of at least a portion of adynamically-generated dashboard file 1350 that generally corresponds tothe source dashboard file 1300 and the dashboard view 1200. For example,the dynamically-generated dashboard file 1350 can be generated based onthe source dashboard file 1300, and, when executed, can generate andcause the display of a dynamically-generated dashboard view 1200.

As illustrated, the dynamically-generated dashboard file 1350 can differfrom the source dashboard file 1300 in many respects. For example, thedynamically-generated dashboard file 1350 does not include queryinitiation code 1306, 1308 the queries 1302, 1304, or references to thequery initiation code 1306, 1308 or queries 1302, 1304. Further, thedynamically-generated dashboard file 1350 includes inlined query results1352 from the execution of the queries 1302, 1304 and inlinedvisualization code 1354A, 1354B that can generate and cause display ofthe visualizations 1202-1206 without reference to other libraries orfiles. In some embodiments, the visualization code can include stylevisualization code (code between <style> and </style>) and scriptvisualization code (code between <script> and </script>). In certainembodiments, the style visualization code 1354A can be used to determinehow the UI will be displayed (e.g., color, placement, look and feel) andthe script visualization code 1354B can be used to provide thefunctionality of the UI (e.g., interactive functionality, execution,etc.). In the illustrated embodiment of FIG. 13, the query results 1352are included as part of the script visualization code. However, it willbe understood that the query results 1352 can be located in a variety oflocations within the dynamically-generated dashboard file 1350.

In addition, the dynamically-generated dashboard file 1350 includescomputer-executable instructions in a different format or differentcomputer language than the computer-executable instructions found in thesource dashboard file. For example, in the illustrated embodiment ofFIG. 13A, the source dashboard file 1300 includes HTML code, whereas,the dynamically-generated dashboard file 1350 includes HTML code, CSScode, and JavaScript code. Although not illustrated by FIGS. 13A and13B, the dynamically-generated dashboard file 1350 can be significantlylonger and larger than the source dashboard file 1300. For example,where the source dashboard file 1300 may include less than one hundredor a few hundred lines of code, the dynamically-generated dashboard file1350 can include over one thousand lines of code.

In some embodiments, the dynamically-generated dashboard file 1350 canbe referred to as a self-contained dashboard file because it includesall the code and data needed to generate and display the visualizations1202-1206. In other words, the dynamically-generated dashboard file 1350can generate and display the visualizations 1202-1206 without looking upor accessing additional files or libraries.

In some embodiments, the dynamically-generated dashboard file 1350 mayinclude queries, query initiation code, may not be self-contained and/orcan reference other files or libraries to generate and display thevisualizations. For instance, the computing devices can generatemultiple files that can be used in combination to generate and displaythe visualizations. As a non-limiting example, one generated file caninclude the query results, another generated file can include queryinitiation code that is used to access the query results, anothergenerated file can include UI formatting information, and anothergenerated file can include the libraries to effectuate the interactivefunctionality. It will be understood that any combination of generatedfiles can be used to generate and display the visualizationscorresponding to the source dashboard file.

Furthermore, in some embodiments, the computing devices can makeavailable to the dynamically-generated dashboard file the files andlibraries used by the source dashboard file to generate thevisualizations. For example, the computing devices can place copies ofthe libraries or files at a location that a computing device executingthe dynamically-generated dashboard file can access. Accordingly, insome embodiments, the dynamically-generated dashboard file can besimilar to the source dashboard file (non-limiting examples: includequeries and/or query initiation code, use the same computer language, beapproximately the same length, etc.), but can access the (static) queryresults without accessing the data intake and query system or withoutexecuting the queries against one or more time-series data stores eachtime the dynamically-generated dashboard file is accessed.

In some embodiments, the computing devices can enable a user to updatethe visualizations with updated query results at predetermined timeperiods. For example, a user can designate that the query results (andvisualizations) are to be updated every minute, every five minutes, etc.In some embodiments, each time the query results are updated, thecomputing devices generate a new dynamically-generated dashboard file orfiles. In some embodiments, such as when the query results are stored ina separate file, the computing devices do not generate a newdynamically-generated dashboard file, but indicate that new queryresults are available for visualization. Accordingly, thedynamically-generated dashboard file can access the updated queryresults for display.

In certain embodiments, the visualizations rely on different queryresults for display. For example, for each query found in the sourcedashboard file, the computing devices can initiate a separate query,even if the queries are the same. Further, the query results of eachquery can be stored in the dynamically-generated dashboard file.

In some embodiments, one or more visualizations rely on the same queryresults. In some embodiments, the computing devices can identify thesame or similar queries in the source dashboard file, initiate a singlequery, and store the query results once in the dynamically-generateddashboard file. The visualization code that relies on the query resultscan reference the same query results in the dynamically-generateddashboard file. Further, in some cases, the source dashboard files caninclude visualizations that reference the same query. For example, aquery can be a global query and the different visualizations canreference the global query. In some cases, the dashboard files caninclude partial queries, which can further process the results of aquery, such as a global query.

In certain embodiments, the dynamically-generated dashboard fileperforms processing on the (static) query results. For example, if avisualization is to display an average of query results, thedynamically-generated dashboard file can calculate the average inreal-time upon execution of the dynamically-generated dashboard file. Incertain embodiments, the average is calculated prior to or during thegeneration of the dynamically-generated dashboard file and is“hard-coded” into the dynamically-generated dashboard file.

Furthermore, in some embodiments, the dynamically-generated dashboardfile enables a user to perform additional post-processing on the queryresults. For example, the source dashboard file may not have included avisualization of an average, minimum, or maximum, of the query results,but the dynamically-generated dashboard file can enable a user tocalculate the average, minimum, or maximum of the query results usingthe static query results that were obtained as part of generating thedynamically-generated dashboard file. It will be understood thatadditional processing of the query results in the dynamically-generateddashboard file can be provided as desired.

Further, in some embodiments, the dynamically-generated dashboard filecan be generated without parsing a source dashboard file. For example, auser can specify one or more queries and one or more visualizations ofthe query results. The computing devices can receive the identificationof the specified queries and visualizations and use the specifiedqueries and visualizations to generate the dynamically-generateddashboard file by initiating the specified queries and referring to thevarious definitions, descriptions, and libraries used to define thevisualizations.

FIG. 14 is a flow diagram illustrative of an embodiment of a routine1400 executed by one or more computing devices for generating a set ofone or more files. In some embodiments, the routine 1400 is executed byone or more computing devices of the data intake and query system and/orone or more computing device in communication with the data intake andquery system.

At block 1402, the computing devices parse one or more files. Asdescribed in greater detail above, in some cases, the files aredashboard files. In some cases, as part of parsing the files, thecomputing devices identify queries to be initiated and visualizationsand interactive functionality to retain or discard. In certainembodiments, the computing devices can access relevant files to obtaincomputer-executable instructions to render and display thevisualizations or to provide the interactive functionality. In someembodiments, the computing devices can use one or more configurationfiles to determine what actions are to be taken with the code identifiedin the files. Further, in some cases, the computing devices can parseadditional dashboard files that are referenced by the initial dashboardfile.

At block 1404, the computing devices initiate the queries. In somecases, as described in greater detail above, the computing devices canrequest the data intake and query system to execute the queries.Executing the queries may include spinning up or generating one or moresearch jobs, communicating at least a portion of the query to one ormore indexers, receiving results from the one or more indexers andaggregating the results. In some embodiments, further processing can beperformed on the aggregated results, and the results can be provided tothe computing devices.

At block 1406, the computing devices generate one or moredynamically-generated files. As described in greater detail above, thedynamically-generated files can include the query results andcomputer-executable instructions to display the query results. Incertain embodiments, the query results are inlined into thedynamically-generated files. In some cases, by accessing thedynamically-generated files, the query results are displayed invisualizations that are the same as the visualizations referenced ordescribed in the source dashboard file. In certain embodiments thedynamically-generated files are self-contained in that they do notreference other files or libraries for execution. In some embodiments,the dynamically-generated files reference other files in order togenerate and display the visualizations. The visualizations can use thesame query results or each can rely on its own query results. In somecases, the dynamically-generated files are HTML files or client-sidescripting files. In some embodiments, the dynamically-generated filesinclude inlined computer-executable instructions to generate and displaythe visualizations and to provide the interactive functionality.

At block 1408, the computing devices save the one or moredynamically-generated files in a data store accessible to a user. Asdescribed in greater detail above, the dynamically-generated files canbe stored in a location such that a user can access them withoutaccessing the data intake and query system. For example, thedynamically-generated files can be stored in an email communicated tothe user, on an intranet or public server accessible by the user, or ina data store accessible by the user. In this way, the user can accessthe dynamically-generated UI without accessing the data intake and querysystem.

It will be understood that the routine 1400 can include different,fewer, or more blocks as desired. For example, the routine 1400 caninclude receiving an identification of one or more queries and one ormore visualizations, and generating one or more dynamically-generatedfiles based on the identified one or more queries and one or morevisualizations. In certain embodiments, the routine 1400 can includereceiving the dashboard files from the data intake and query system. Insome embodiments, the routine 1400 can include receiving indications ofqueries, visualizations, and interactive functionality to retain for thedynamically-generated files and receiving indications of queries,visualizations, and interactive functionality that are to be discarded.

FIG. 15 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented. For example, the hardware architecture of a computingsystem 72 can be used to implement any one or more of the functionalcomponents described herein (e.g., indexer, data intake and querysystem, search head, data store, server computer system, edge device,etc.). In some embodiments, one or multiple instances of the computingsystem 72 can be used to implement the techniques described herein,where multiple such instances can be coupled to each other via one ormore networks.

The illustrated computing system 72 includes one or more processingdevices 74, one or more memory devices 76, one or more communicationdevices 78, one or more input/output (I/O) devices 80, and one or moremass storage devices 82, all coupled to each other through aninterconnect 84. The interconnect 84 may be or include one or moreconductive traces, buses, point-to-point connections, controllers,adapters, and/or other conventional connection devices. Each of theprocessing devices 74 controls, at least in part, the overall operationof the processing of the computing system 72 and can be or include, forexample, one or more general-purpose programmable microprocessors,digital signal processors (DSPs), mobile application processors,microcontrollers, application-specific integrated circuits (ASICs),programmable gate arrays (PGAs), or the like, or a combination of suchdevices.

Each of the memory devices 76 can be or include one or more physicalstorage devices, which may be in the form of random access memory (RAM),read-only memory (ROM) (which may be erasable and programmable), flashmemory, miniature hard disk drive, or other suitable type of storagedevice, or a combination of such devices. Each mass storage device 82can be or include one or more hard drives, digital versatile disks(DVDs), flash memories, or the like. Each memory device 76 and/or massstorage device 82 can store (individually or collectively) data andinstructions that configure the processing device(s) 74 to executeoperations to implement the techniques described above.

Each communication device 78 may be or include, for example, an Ethernetadapter, cable modem, Wi-Fi adapter, cellular transceiver, basebandprocessor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or thelike, or a combination thereof. Depending on the specific nature andpurpose of the processing devices 74, each I/O device 80 can be orinclude a device such as a display (which may be a touch screendisplay), audio speaker, keyboard, mouse or other pointing device,microphone, camera, etc. Note, however, that such I/O devices 80 may beunnecessary if the processing device 74 is embodied solely as a servercomputer.

In the case of a client device (e.g., edge device), the communicationdevices(s) 78 can be or include, for example, a cellulartelecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fitransceiver, baseband processor, Bluetooth or BLE transceiver, or thelike, or a combination thereof. In the case of a server, thecommunication device(s) 78 can be or include, for example, any of theaforementioned types of communication devices, a wired Ethernet adapter,cable modem, DSL modem, or the like, or a combination of such devices.

A software program or algorithm, when referred to as “implemented in acomputer-readable storage medium,” includes computer-readableinstructions stored in a memory device (e.g., memory device(s) 76). Aprocessor (e.g., processing device(s) 74) is “configured to execute asoftware program” when at least one value associated with the softwareprogram is stored in a register that is readable by the processor. Insome embodiments, routines executed to implement the disclosedtechniques may be implemented as part of OS software (e.g., MICROSOFTWINDOWS® and LINUX®) or a specific software application, algorithmcomponent, program, object, module, or sequence of instructions referredto as “computer programs.”

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor (e.g., processingdevice(s) 74), will cause a computing device to execute functionsinvolving the disclosed techniques. In some embodiments, a carriercontaining the aforementioned computer program product is provided. Thecarrier is one of an electronic signal, an optical signal, a radiosignal, or a non-transitory computer-readable storage medium (e.g., thememory device(s) 76).

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

1. A method, comprising: parsing a first set of one or more files toidentify a query, the first set of one or more files comprising: thequery identifying a set of data to be processed and a manner ofprocessing the set of data, and first visualization code comprisingcomputer-executable instructions that when executed cause one or moreprocessors to display a visualization of results of the query;initiating the query; generating a second set of one or more filescomprising: the results of the query, and second visualization codecomprising computer-executable instructions that when executed cause oneor more processors to display the visualization, wherein the secondvisualization code is generated based on the first visualization code;and storing the second set of one or more files at a data storeaccessible to a user.
 2. The method of claim 1, wherein obtaining theresults of the query requires access to a data intake and query systemthat executes the query.
 3. The method of claim 1, wherein obtaining theresults of the query requires access to a data intake and query systemthat executes the query and the data intake and query system is notaccessible to the user.
 4. The method of claim 1, wherein each time thefirst set of one or more files is accessed the query is executed by adata intake and query system.
 5. The method of claim 1, furthercomprising, following a predetermined time period: generating a thirdset of one or more files comprising: updated results of the query, andthird visualization code comprising computer-executable instructionsthat when executed cause one or more processors to display thevisualization based on the updated results of the query; and storing thethird set of one or more files at the data store accessible to the user.6. The method of claim 1, further comprising receiving the results ofthe query from a data intake and query system.
 7. The method of claim 1,wherein the first visualization code references computer-executableinstructions in one or more distinct files that are used to generate thesecond visualization code.
 8. The method of claim 1, wherein the firstvisualization code references computer-executable instructions that whenexecuted cause one or more processors to implement interactivefunctionality with the visualization.
 9. The method of claim 1, whereinthe first visualization code includes computer-executable instructionsthat when executed cause the one or more processors to implementinteractive functionality with the visualization.
 10. The method ofclaim 1, wherein the second visualization code includecomputer-executable instructions that when executed cause the one ormore processors to implement zoom-in functionality for at least aportion of the visualization.
 11. The method of claim 1, wherein thesecond visualization code include computer-executable instructions thatwhen executed cause the one or more processors to implement tooltipfunctionality for at least a portion of the visualization.
 12. Themethod of claim 1, wherein the first visualization code includesinteractivity code that references one or more files comprisingcomputer-executable instructions that when executed cause the one ormore processors to implement interactive functionality with thevisualization, wherein the parsing comprises identifying certaininteractive functionality for discarding, and wherein generating thesecond set of one or more files comprises generating the secondvisualization code without computer-executable instructions forimplementing the certain interactive functionality.
 13. The method ofclaim 1, wherein the first visualization code includes interactivitycode that references one or more files comprising computer-executableinstructions that when executed cause the one or more processors toimplement interactive functionality with the visualization, wherein theparsing comprises identifying certain interactive functionality fordiscarding based on a determination that the certain interactivefunctionality requires access to a data intake and query system thatprovides the results of the query, and wherein generating the second setof one or more files comprises generating the second visualization codewithout computer-executable instructions for implementing the certaininteractive functionality.
 14. The method of claim 1, wherein the firstvisualization code includes first interactivity code that references oneor more files comprising computer-executable instructions that whenexecuted cause the one or more processors to implement interactivefunctionality with the visualization, and wherein the secondvisualization code includes the computer-executable instructionsreferenced in the one or more files.
 15. The method of claim 1, whereinthe first visualization code includes first interactivity code thatreferences one or more files comprising computer-executable instructionsthat when executed cause the one or more processors to implementinteractive functionality with the visualization, and wherein the secondvisualization code includes second interactivity code that is generatedbased on the computer-executable instructions referenced by the firstinteractivity code.
 16. The method of claim 1, wherein the query is afirst query, the set of data is a first set of data, the visualizationis a first visualization, and the one or more first set of filescomprises a reference to a third set of one or more files, the third setof one or more files comprising: a second query identifying a second setof data to be processed and a manner of processing the second set ofdata, and third visualization code comprising computer-executableinstructions that when executed cause one or more processors to displaya second visualization of results of the second query, the methodfurther comprising: parsing the third set of one or more files toidentify the second query; initiating the second query; generating afourth set of one or more files comprising: the results of the secondquery, and fourth visualization code comprising computer-executableinstructions that when executed cause one or more processors to displaythe second visualization; and storing the fourth set of one or morefiles at the data store accessible to the user.
 17. The method of claim1, wherein the first set of one or more files further comprises queryinitiation code comprising computer-executable instructions that whenexecuted further cause the one or more processors to initiate the query.18. The method of claim 1, wherein the first set of one or more filesfurther comprises computer-executable instructions that when executedfurther cause the one or more processors to initiate the query, andwherein the second set of one or more files does not includecomputer-executable instructions that when executed further cause theone or more processors to initiate the query.
 19. The method of claim 1,wherein accessing the second set of one or more files causes the resultsof the query to be displayed without executing the query.
 20. The methodof claim 1, wherein the second set of one or more files enable the userto filter the results of the query.
 21. The method of claim 1, whereinthe second set of one or more files enable the user to process theresults of the query.
 22. The method of claim 1, wherein a first file ofthe second set of one or more files stores the results of the query anda second file of the second set of one or more files stores the secondvisualization code.
 23. The method of claim 1, wherein the secondvisualization code comprises computer-executable instructions in adifferent computer language than computer-executable instructions of thefirst visualization code.
 24. The method of claim 1, wherein the secondset of one or more files includes an HTML file comprising inlinedcomputer-executable instructions in a different computer language. 25.The method of claim 1, wherein the second set of one or more filesincludes an HTML file comprising the results of the query inlined. 26.The method of claim 1, wherein the query is a first query and thevisualization is a first visualization, wherein the first set of one ormore files further comprise a second query identifying the set of datato be processed and the manner of processing the set of data, and thefirst visualization code further comprises computer-executableinstructions that when executed cause one or more processors to displaya second visualization of results of the second query, wherein the firstset of one or more files are further parsed to identify the secondquery, the method further comprising: determining not to initiate thesecond query based on a determination that the second query matches thefirst query, wherein the second visualization code further comprisescomputer-executable instructions that when executed cause one or moreprocessors to display the second visualization based on the results ofthe first query.
 27. The method of claim 1, wherein the query is a firstquery and the visualization is a first visualization, wherein the firstset of one or more files further comprise a second query identifying theset of data to be processed and the manner of processing the set ofdata, and the first visualization code further comprisescomputer-executable instructions that when executed cause one or moreprocessors to display a second visualization of results of the secondquery, wherein the first set of one or more files are further parsed toidentify the second query, the method further comprising: initiating thesecond query, wherein the second set of one or more files furthercomprises: the results of the second query, and wherein the secondvisualization code further comprises computer-executable instructionsthat when executed cause one or more processors to display the secondvisualization.
 28. The method of claim 1, wherein the visualization is afirst visualization, and the first visualization code further comprisescomputer-executable instructions that when executed cause one or moreprocessors to display a second visualization of the results of thequery, and wherein the second visualization code further comprisescomputer-executable instructions that when executed cause one or moreprocessors to display the second visualization.
 29. A computing system,comprising: one or more processing devices configured to: parse a firstset of one or more files to identify a query, the first set of one ormore files comprising: the query identifying a set of data to beprocessed and a manner of processing the set of data, and firstvisualization code comprising computer-executable instructions that whenexecuted cause one or more processors to display a visualization ofresults of the query; initiate the query; generate a second set of oneor more files comprising: the results of the query, and secondvisualization code comprising computer-executable instructions that whenexecuted cause one or more processors to display the visualization,wherein the second visualization code is generated based on the firstvisualization code; and store the second set of one or more files at adata store accessible to a user.
 30. Non-transitory computer readablemedia comprising computer-executable instructions that, when executed bya computing system, cause the computing system to: parse a first set ofone or more files to identify a query, the first set of one or morefiles comprising: the query identifying a set of data to be processedand a manner of processing the set of data, and first visualization codecomprising computer-executable instructions that when executed cause oneor more processors to display a visualization of results of the query;initiate the query; generate a second set of one or more filescomprising: the results of the query, and second visualization codecomprising computer-executable instructions that when executed cause oneor more processors to display the visualization, wherein the secondvisualization code is generated based on the first visualization code;and store the second set of one or more files at a data store accessibleto a user.